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import os
import requests
from langchain.tools import tool
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from sec_api import QueryApi
from unstructured.partition.html import partition_html
class SECTools():
@tool("Search 10-Q form")
def search_10q(data):
"""
Useful to search information from the latest 10-Q form for a
given stock.
The input to this tool should be a pipe (|) separated text of
length two, representing the stock ticker you are interested, what
question you have from it.
For example, `AAPL|what was last quarter's revenue`.
"""
stock, ask = data.split("|")
queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
query = {
"query": {
"query_string": {
"query": f"ticker:{stock} AND formType:\"10-Q\""
}
},
"from": "0",
"size": "1",
"sort": [{ "filedAt": { "order": "desc" }}]
}
filings = queryApi.get_filings(query)['filings']
link = filings[0]['linkToFilingDetails']
answer = SECTools.__embedding_search(link, ask)
return answer
@tool("Search 10-K form")
def search_10k(data):
"""
Useful to search information from the latest 10-K form for a
given stock.
The input to this tool should be a pipe (|) separated text of
length two, representing the stock ticker you are interested, what
question you have from it.
For example, `AAPL|what was last year's revenue`.
"""
stock, ask = data.split("|")
queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
query = {
"query": {
"query_string": {
"query": f"ticker:{stock} AND formType:\"10-K\""
}
},
"from": "0",
"size": "1",
"sort": [{ "filedAt": { "order": "desc" }}]
}
filings = queryApi.get_filings(query)['filings']
link = filings[0]['linkToFilingDetails']
answer = SECTools.__embedding_search(link, ask)
return answer
def __embedding_search(url, ask):
text = SECTools.__download_form_html(url)
elements = partition_html(text=text)
content = "\n".join([str(el) for el in elements])
text_splitter = CharacterTextSplitter(
separator = "\n",
chunk_size = 1000,
chunk_overlap = 150,
length_function = len,
is_separator_regex = False,
)
docs = text_splitter.create_documents([content])
retriever = FAISS.from_documents(
docs, OpenAIEmbeddings()
).as_retriever()
answers = retriever.get_relevant_documents(ask, top_k=4)
answers = "\n\n".join([a.page_content for a in answers])
return answers
def __download_form_html(url):
headers = {
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
'Accept-Encoding': 'gzip, deflate, br',
'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7',
'Cache-Control': 'max-age=0',
'Dnt': '1',
'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"',
'Sec-Ch-Ua-Mobile': '?0',
'Sec-Ch-Ua-Platform': '"macOS"',
'Sec-Fetch-Dest': 'document',
'Sec-Fetch-Mode': 'navigate',
'Sec-Fetch-Site': 'none',
'Sec-Fetch-User': '?1',
'Upgrade-Insecure-Requests': '1',
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
}
response = requests.get(url, headers=headers)
return response.text |